The driving factors have a critical effect on shaping stakeholder behavior toward participating in decision-making for river restoration initiatives. The participation of stakeholders is a vital determinant for increasing public confidence in the government and enhancing the acceptance of government decisions. Conversely, insufficient stakeholder participation in decision-making may lead to resistance to decisions on river restoration projects. Thus, the primary purpose of this investigation is to shed light on the complex interactions between the various drivers that underpin stakeholder participation in the context of the Moat System Restoration Project (MSRP). The extended Theory of Planned Behavior (TPB) describes the relationships between seven drivers that have positively influenced stakeholder participation behaviors: stakeholder attitude, priority, risk perception, trust in government decisions, motivation, intention, and knowledge. The empirical underpinning of this research was obtained through a questionnaire survey conducted in Tianchang, China, encompassing a sample size of 473. The empirical findings discern that stakeholder attitudes vis-à-vis the MSRP favorably influence stakeholder participation behaviors. Additionally, stakeholder motivation and intention have been discerned as catalysts for heightened stakeholder participation behavior. These findings promise to furnish invaluable insights, benefit forthcoming river restoration initiatives, and equip decision-makers with a profound understanding of strategies to enhance stakeholder participation.
To develop a research overview of brain tumor classification using machine learning, we conducted a systematic review with a bibliometric analysis. Our systematic review and bibliometric analysis included 1747 studies of automated brain tumor detection using machine learning reported in the previous 5 years (2019-2023) from 679 different sources and authored by 6632 investigators. Bibliographic data were collected from the Scopus database, and a comprehensive bibliometric analysis was conducted using Biblioshiny and the R platform. The most productive and collaborative institutes, reports, journals, and countries were determined using citation analysis. In addition, various collaboration metrics were determined at the institute, country, and author level. Lotka's law was tested using the authors' performance. Analysis showed that the authors' publication trends followed Lotka's inverse square law. An annual publication analysis showed that 36.46% of the studies had been reported in 2022, with steady growth from previous years. Most of the cited authors had focused on multiclass classification and novel convolutional neural network models that are efficient for small training sets. A keyword analysis showed that "deep learning," "magnetic resonance imaging," "nuclear magnetic resonance imaging," and "glioma" appeared most often, proving that of the several brain tumor types, most studies had focused on glioma. India, China, and the United States were among the highest collaborative countries in terms of both authors and institutes. The University of Toronto and Harvard Medical School had the highest number of affiliations with 132 and 87 publications, respectively.
This paper proposes a novel hybrid arithmetic-trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.